Published by Microsoft Press (March 10, 2021) © 2020
Dino Esposito | Francesco EspositoMaster machine learning concepts and develop real-world solutions
Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning.
· 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you
· Explore what’s known about how humans learn and how intelligent software is built
· Discover which problems machine learning can address
· Understand the machine learning pipeline: the steps leading to a deliverable model
· Use AutoML to automatically select the best pipeline for any problem and dataset
· Master ML.NET, implement its pipeline, and apply its tasks and algorithms
· Explore the mathematical foundations of machine learning
· Make predictions, improve decision-making, and apply probabilistic methods
· Group data via classification and clustering
· Learn the fundamentals of deep learning, including neural network design
· Leverage AI cloud services to build better real-world solutions faster
About This Book
· For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills
· Includes examples of machine learning coding scenarios built using the ML.NET library
- Chapter 1 How Humans Learn
- Chapter 2 Intelligent Software
- Chapter 3 Mapping Problems and Algorithms
- Chapter 4 General Steps for a Machine Learning Solution
- Chapter 5 The Data Factor
- Chapter 6 The .NET Way
- Chapter 7 Implementing the ML.NET Pipeline
- Chapter 8 ML.NET Tasks and Algorithms
- Chapter 9 Math Foundations of Machine Learning
- Chapter 10 Metrics of Machine Learning
- Chapter 11 How to Make Simple Predictions: Linear Regression
- Chapter 12 How to Make Complex Predictions and Decisions: Trees
- Chapter 13 How to Make Better Decisions: Ensemble Methods
- Chapter 14 Probabilistic Methods: Naïve Bayes
- Chapter 15 How to Group Data: Classification and Clustering
- Chapter 16 Feed-Forward Neural Networks
- Chapter 17 Design of a Neural Network
- Chapter 18 Other Types of Neural Networks
- Chapter 19 Sentiment Analysis: An End-to-End Solution
- Chapter 20 AI Cloud Services for the Real World
- Chapter 21 The Business Perception of AI